Definition and application of expert knowledge on vegetation pattern,phenology, and seasonality for habitat mapping,as exemplified in a Mediterranean coastal site

Habitats are effective indicators of biodiversity. Remote sensing data and techniques are of great utility for their long-term monitoring. Habitat maps can be derived from land cover (LC) maps through rules obtained from expert knowledge and integrated with in situ data. Spatial (vegetation pattern) and temporal (phenology and water seasonality) relationships were explored and documented to infer reliable rules for LC (according to the Food and Agricultural Organization Land Cover Classification System (FAO-LCCS) taxonomy) to habitat (Annex I to the 92/43 EEC Directive and EUNIS) class translation. A coastal site in southern Italy was considered as study site for the definition and validation of such rules. Phenological data of the plant communities were collected on the basis of vegetation plots randomly distributed within the study site. Water seasonality was extracted from periodical observation of the water surface. Vegetation pattern was analyzed by means of vegetation survey along transects. The potentiality of rules, based on this specific expert knowledge, was tested in an experimental setting for habitat mapping. The overall accuracy of the habitat map was 75.1%. Such a result supports the usefulness of prior expert knowledge for habitat mapping from LCCS classes and disambiguation on one-to-many relations between LC/LU and habitat types.     

A Land System representation for global assessments and land‐use modeling

Current global scale land‐change models used for integrated assessments and climate modeling are based on classifications of land cover. However, land‐use management intensity and livestock keeping are also important aspects of land use, and are an integrated part of land systems. This article aims to classify, map, and to characterize Land Systems (LS) at a global scale and analyze the spatial determinants of these systems. Besides proposing such a classification, the article tests if global assessments can be based on globally uniform allocation rules. Land cover, livestock, and agricultural intensity data are used to map LS using a hierarchical classification method. Logistic regressions are used to analyze variation in spatial determinants of LS. The analysis of the spatial determinants of LS indicates strong associations between LS and a range of socioeconomic and biophysical indicators of human‐environment interactions. The set of identified spatial determinants of a LS differs among regions and scales, especially for (mosaic) cropland systems, grassland systems with livestock, and settlements. (Semi‐)Natural LS have more similar spatial determinants across regions and scales. Using LS in global models is expected to result in a more accurate representation of land use capturing important aspects of land systems and land architecture: the variation in land cover and the link between land‐use intensity and landscape composition. Because the set of most important spatial determinants of LS varies among regions and scales, land‐change models that include the human drivers of land change are best parameterized at sub‐global level, where similar biophysical, socioeconomic and cultural conditions prevail in the specific regions.     

A Simple Semi-Automatic Approach for Land Cover Classification from Multispectral Remote Sensing Imagery

Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization) with convenience.     

Harmonization of the Land Cover Classification System (LCCS) with the General Habitat Categories (GHC) classification system

Monitoring land cover and habitat change is a key issue for conservation managers because of its potential negative impact on biodiversity. The Land Cover Classification System (LCCS) and the General Habitat Categories (GHC) System have been proposed by the remote sensing and ecological research community, respectively, for the classification of land covers and habitats across various scales. Linking the two systems can be a major step forward towards biodiversity monitoring using remote sensing. The translation between the two systems has proved to be challenging, largely because of differences in definitions and related difficulties in creating one-to-one relationships between the two systems. This paper proposes a system of rules for linking the two systems and additionally identifies requirements for site-specific contextual and environmental information to enable the translation. As an illustration, the LCCS classification of the Le Cesine protected area in Italy is used to show rules for translating the LCCS classes to GHCs. This study demonstrates the benefits of a translation system for biodiversity monitoring using remote sensing data but also shows that a successful translation is often depending on the degree of ecological knowledge of the habitats and its relationship with land cover and contextual information.     

Role of land cover changes for atmospheric CO2 increase and climate change during the last 150 years

We assess the role of changing natural (volcanic, aerosol, insolation) and anthropogenic (CO₂ emissions, land cover) forcings on the global climate system over the last 150 years using an earth system model of intermediate complexity, CLIMBER‐2. We apply several datasets of historical land‐use reconstructions: the cropland dataset by Ramankutty & Foley (1999) (R&F), the HYDE land cover dataset of Klein Goldewijk (2001) , and the land‐use emissions data from Houghton & Hackler (2002) . Comparison between the simulated and observed temporal evolution of atmospheric CO₂ and δ¹³CO₂ are used to evaluate these datasets. To check model uncertainty, CLIMBER‐2 was coupled to the more complex Lund–Potsdam–Jena (LPJ) dynamic global vegetation model. In simulation with R&F dataset, biogeophysical mechanisms due to land cover changes tend to decrease global air temperature by 0.26°C, while biogeochemical mechanisms act to warm the climate by 0.18°C. The net effect on climate is negligible on a global scale, but pronounced over the land in the temperate and high northern latitudes where a cooling due to an increase in land surface albedo offsets the warming due to land‐use CO₂ emissions. Land cover changes led to estimated increases in atmospheric CO₂ of between 22 and 43 ppmv. Over the entire period 1800–2000, simulated δ¹³CO₂ with HYDE compares most favourably with ice core during 1850–1950 and Cape Grim data, indicating preference of earlier land clearance in HYDE over R&F. In relative terms, land cover forcing corresponds to 25–49% of the observed growth in atmospheric CO₂. This contribution declined from 36–60% during 1850–1960 to 4–35% during 1960–2000. CLIMBER‐2‐LPJ simulates the land cover contribution to atmospheric CO₂ growth to decrease from 68% during 1900–1960 to 12% in the 1980s. Overall, our simulations show a decline in the relative role of land cover changes for atmospheric CO₂ increase during the last 150 years.     

基于长时间序列landsat数据的科尔沁沙地土地利用演变分析

以科尔沁沙地沙丘-草甸过渡带区域主要土地覆被类型为研究对象,以1987-2017年多时相Landsat TM/OLI遥感影像解译分类为基础,参考生态学植被演替研究方法,系统分析研究区30年来的土地利用/覆被动态演变规律,研究结果表明：（1）决策树法在复杂下垫面不同覆被类型的同步识别效果较好,所有影像分类精度均达到88%以上,分类效果较好,其中2017年分类精度最高为95.24%,达到了分类研究的要求;（2）研究区存在着"半灌丛-草甸地-灌丛"的植被结构特征,且整体表现为"南进北退"的变化趋势。结合土地利用动态度分析结果表明人类活动干涉下,研究区整体上遵循了半干旱区植被条件改善的一般规律,侧面反映该研究区域生态环境的持续不稳定性和脆弱性;（3）研究区覆被类型发生变化的总面积达到2623.59 hm²,总变化强度为63.76%。其中正向演替的比例为52.61%,以半灌丛面积的持续减小与沙地草甸面积的持续扩张为主要变化特征。但同时,半灌丛转为沙地的面积为184.95 hm²,表明以放牧为主的研究区同时发生着局部的逆行演变;（4）质心迁移结果反映了1987-2017年间,除人为影响较大的林地、草地以及耕地向北迁移外,其他植被类型的质心都有很明显的南迁,主要植被类型重心迁移距离依次由大到小为耕地 > 半灌丛 > 灌丛 > 沙地草甸 > 湿地草甸 > 林地。研究通过记录科尔沁沙地连续扩展的时空模式,展示了遥感-生态和时间序列影像在30 m分辨率下跟踪土地利用/覆被变化的潜力,为提高干旱半干旱区土地利用情况的动态监测效率,开展土地利用/覆被动态演变研究提供参考。     

Are CORINE land cover classes reliable proxies of plant species assemblages? A test in Mediterranean forest landscapes

In land cover mapping, the complexity of landscapes is fitted into classes that may limit the recognition of natural variability. In this study, we tested the power of land cover classes (defined on the CORINE land cover classification scheme, a standardized legend set by EU for land cover inventory) to separate different vascular plant assemblages in forest ecosystems. In order to separately identify the role of different sources of inconsistency between land cover classes and species composition, we compared three different inventory processes, based on (i) dominant tree species as observed in the field, (ii) visual interpretation of remotely sensed images and (iii) semi-automatic supervised classification of satellite images. Our results underline that classifying forest ecosystems on the basis of their canopy species produces an over-simplification of habitat variability. Consequently, land cover maps based on non-specialized classification schemes should not be regarded as good proxies for plant biodiversity. If land cover maps are intended to describe and manage landscapes and their associated biodiversity, it is necessary to improve their capacity to represent the complexity of ecosystems.     

Permeability of the landscape matrix between amphibian breeding sites

For organisms that reproduce in discrete habitat patches, land cover between patches (known as the matrix) is important for dispersal among breeding sites. Models of patchy populations often incorporate information on the permeability of the matrix to dispersal, sometimes based on expert opinion. I estimated the relative resistance to gene flow of land cover types and barriers using F_ST calculated from microsatellite markers in two amphibians, within an 800‐km² area in northern Switzerland. The species included a frog (Rana temporaria: 996 individuals, 48 populations, seven markers) and a newt (Triturus alpestris: 816 individuals, 41 populations, seven markers). Open fields and urban areas were more resistant to gene flow than forested land; roads and highways also reduced permeability. Results were similar for the two species. However, differences in resistance among matrix elements were relatively low: gene flow through urban areas was reduced by only 24–42% relative to forest; a divided highway reduced gene flow by 11–40% and was 7–8 times more resistant than a secondary road. These data offer an empirically based alternative to expert opinion for setting relative resistance values in landscape models.     

Dove Habitat Association Based on Remotely Sensed Land Cover Types in South Texas

Abstract: We tested whether presence of white-winged doves (Zenaida asiatica) and mourning doves (Z. macroura) in South Texas, USA, was associated with any of the land cover types recorded in the 2001 National Land Classification Database. We used the point-transect method to conduct presence—absence surveys for both species at 236 points encompassing 744 observations. Within predefined land cover types surrounding each survey point, we used Geographic Information Systems to determine the proportions of each land cover type present. We used randomization tests to compare proportions of land cover types present at points with and without doves. We used Program DISTANCE to estimate dove densities at survey points and to test whether certain land cover types were associated with greater dove densities. Our results indicated white-winged dove occurrence in South Texas was positively associated with urban land and cropland, whereas mourning dove occurrence was positively associated with cropland. For land cover types found to be associated with dove presence, estimated density for each dove species increased as the proportion of associated land cover type increased. These results can assist wildlife biologists in the development of a more efficient and targeted protocol for censusing doves. In addition, our methods can be applied to other species across several geographic areas and landscape scales.     

发展NECT土地覆盖特征数据集的原理、方法和应用

着重探讨了建立中国东北样带 (NortheastChinatransect, NECT) 土地覆盖特征数据集的原理、方法及其在全球变化研究方面的重要应用。NECT土地覆盖特征数据集是以多时相的 1km分辨率的NOAA/AVHRR归一化植被指数NDVI (Normalizeddifferencevegetationindex) 数字影像为基础, 同时采用高程、气候、土壤、植被、土地利用、土地资源、生态区域、行政边界、经济、社会等多源数据作为数据源, 并经过标准化处理 (如数字化、空间插值、几何配准、投影转换 ) 集成而成。在土地覆盖特征数据集的主要应用方面, 如 :1) 利用多时相、1km分辨率的NOAA/AVHRR影像完成了中国东北样带土地覆盖分类图。一级分类系统包括森林、草原、荒漠和沙地、灌丛、农田、混合覆盖 类型、城镇和水体等 8类, 二级分类体系包括 12类。经过地面采样进行精度检验, 分类精度达到 81.6 1%。 2 ) 对主要植被类型的植物生长季变化进行的研究。利用多时相的遥感影像构造了能够反映植被年际、季节生长变化的遥感植被指数ND VImax、NDVI变幅xam以及NDVI的标准偏差x′s 等, 分析这 3个参数 1983~ 1999年的 17年中的变化情况。该数据集的建立是研究该样带土地覆盖特征及其变化规律的基础, 对基于样带的全球变化研究有重要的意义。     

A retrospective analysis of land cover change using a polygon shape index

Aim This study tests the hypothesis that the propensity of land cover patches to change is related to their shape and geometric complexity. Location The analysis is based on a 1000‐km² area of the Cairngorms in Scotland, incorporating part of Speyside and the high plateau area within the Grampian Mountains. Methods A combined dataset was created by intersecting 1964 land cover data (derived from archive aerial photography) and 1988 land cover data (from the Land Cover of Scotland dataset). A shape index was calculated for each land cover polygon inside a GIS. Information on land cover change was analysed with reference to land cover class and the polygon shape index using a regression analysis. Results For upland seminatural land cover classes, subject to low levels of management, change is related to polygon shape, such that the more complex patches were found to be more susceptible to change. This relationship breaks down where classes are more intensively managed or have been aggregated into mosaic classes. Conclusions Propensity to change was related to shape index for seminatural land cover classes. This implies that at least some landscape processes, such as anthropogenic disturbance of seminatural land covers, can be linked to ecological theory via measurements of spatial pattern. The study also highlighted some of the cartographic issues involved in estimating changes between land cover classes: there are advantages in replacing the ‘cartographic paradigm’ of comparing two derived datasets (in this case land cover maps) with direct comparison of the digital data — air photographs or satellite imagery. Such a direct approach avoids the compounding of errors introduced by the approximation of each successive air photo as a thematic map.     

Downscaling land‐use data to provide global 30″ estimates of five land‐use classes

Land‐use change is one of the biggest threats to biodiversity globally. The effects of land use on biodiversity manifest primarily at local scales which are not captured by the coarse spatial grain of current global land‐use mapping. Assessments of land‐use impacts on biodiversity across large spatial extents require data at a similar spatial grain to the ecological processes they are assessing. Here, we develop a method for statistically downscaling mapped land‐use data that combines generalized additive modeling and constrained optimization. This method was applied to the 0.5° Land‐use Harmonization data for the year 2005 to produce global 30″ (approx. 1 km²) estimates of five land‐use classes: primary habitat, secondary habitat, cropland, pasture, and urban. The original dataset was partitioned into 61 bio‐realms (unique combinations of biome and biogeographical realm) and downscaled using relationships with fine‐grained climate, land cover, landform, and anthropogenic influence layers. The downscaled land‐use data were validated using the PREDICTS database and the geoWiki global cropland dataset. Application of the new method to all 61 bio‐realms produced global fine‐grained layers from the 2005 time step of the Land‐use Harmonization dataset. Coarse‐scaled proportions of land use estimated from these data compared well with those estimated in the original datasets (mean R²: 0.68 ± 0.19). Validation with the PREDICTS database showed the new downscaled land‐use layers improved discrimination of all five classes at PREDICTS sites (P < 0.0001 in all cases). Additional validation of the downscaled cropping layer with the geoWiki layer showed an R² improvement of 0.12 compared with the Land‐use Harmonization data. The downscaling method presented here produced the first global land‐use dataset at a spatial grain relevant to ecological processes that drive changes in biodiversity over space and time. Integrating these data with biodiversity measures will enable the reporting of land‐use impacts on biodiversity at a finer resolution than previously possible. Furthermore, the general method presented here could be useful to others wishing to downscale similarly constrained coarse‐resolution data for other environmental variables.     

Carbon emissions in the field of land use,land use change,and forestry in the Vietnam mainland

The biennial report of Vietnam includes updated information on the greenhouse gas emissions for the base years which are specified by the United Nations Framework Convention on Climate Change for reporting on greenhouse gas emissions by member nations of the Intergovernmental Panel on Climate Change. The estimation of greenhouse gas emissions in general and carbon emissions in particular in the field of land use, land use change, and forestry using advanced technology to provide the input data was recommended. Remote sensing technology with transparency, multi-time characteristics, and wide coverage is useful in this area. An experiment on carbon emission estimation was carried out based on land cover change over ten years between 2002 and 2012. The results obtained by remote sensing data classification for the land cover categories achieved a reliability of 68% for the year 2002 and 67% for the year 2012. Data in relation to the land cover change, soil zoning, and ecological/climate zoning in the Vietnamese mainland, through the process of integrating, processing, and synthesizing data, and using the reported activity data and carbon emission coefficients, were input into the Agriculture and Land Use Greenhouse Gas Inventory Software for carbon emission estimations based on the quality control and quality assurance work.

     

Inclusion of soil data improves the performance of bioclimatic envelope models for insect species distributions in temperate Europe

Aim To analyse the effect of the inclusion of soil and land‐cover data on the performance of bioclimatic envelope models for the regional‐scale prediction of butterfly (Rhopalocera) and grasshopper (Orthoptera) distributions. Location Temperate Europe (Belgium). Methods Distributional data were extracted from butterfly and grasshopper atlases at a resolution of 5 km for the period 1991–2006 in Belgium. For each group separately, the well‐surveyed squares (n = 366 for butterflies and n = 322 for grasshoppers) were identified using an environmental stratification design and were randomly divided into calibration (70%) and evaluation (30%) datasets. Generalized additive models were applied to the calibration dataset to estimate occurrence probabilities for 63 butterfly and 33 grasshopper species, as a function of: (1) climate, (2) climate and land‐cover, (3) climate and soil, and (4) climate, land‐cover and soil variables. Models were evaluated as: (1) the amount of explained deviance in the calibration dataset, (2) Akaike’s information criterion, and (3) the number of omission and commission errors in the evaluation dataset. Results Information on broad land‐cover classes or predominant soil types led to similar improvements in the performance relative to the climate‐only models for both taxonomic groups. In addition, the joint inclusion of land‐cover and soil variables in the models provided predictions that fitted more closely to the species distributions than the predictions obtained from bioclimatic models incorporating only land‐cover or only soil variables. The combined models exhibited higher discrimination ability between the presence and absence of species in the evaluation dataset. Main conclusions These results draw attention to the importance of soil data for species distribution models at regional scales of analysis. The combined inclusion of land‐cover and soil data in the models makes it possible to identify areas with suitable climatic conditions but unsuitable combinations of vegetation and soil types. While contingent on the species, the results indicate the need to consider soil information in regional‐scale species–climate impact models, particularly when predicting future range shifts of species under climate change.     

Effect of land use conversion on soil organic carbon sequestration in the loess hilly area, loess plateau of China

Changes in land use may alter land cover, which results in carbon stock changes in biomass as well as in the soil. In China’s loess plateau, vegetation restoration has been conducted since 1950s to control soil erosion and improve the ecosystem, with significant investment of money and manpower. Despite these efforts, soil erosion has still been severe. To reduce soil erosion and improve land quality, China initiated another state-funded project, Grain-for-Green, in 1999 in the loess plateau. However, it is not clear how effective this newly initiated project will be. In this study, we evaluated the effect of land-use conversion on soil organic carbon (SOC) and the potential effect of the current project on SOC sequestration in the Anjiapo catchment area of the loess hilly area of the loess plateau in China. This evaluation is based on SOC measurements in cropland versus in other converted land use types. We found that SOC sequestration mainly occurred in the surface soil after land use conversion took place. Land use conversion from cropland to shrubland or wild grassland (i.e. undisturbed land) was better for SOC sequestration than tree plantation in the semi-arid loess hilly area. By using the land use change in the study area as a scenario, the potential contribution of land use change on SOC sequestration due to the Grain-for-Green project was estimated. It was found that this project in the loess plateau of China would be helpful for SOC sequestration if successfully implemented.     

Regression Tree Analysis of satellite and terrain data to guide vegetation sampling and surveys

Abstract. Monitoring of regional vegetation and surface biophysical properties is tightly constrained by both the quantity and quality of ground data. Stratified sampling is often used to increase sampling efficiency, but its effectiveness hinges on appropriate classification of the land surface. A good classification must be sufficiently detailed to include the important sources of spatial variability, but at the same time it should be as parsimonious as possible to conserve scarce and expensive degrees of freedom in ground data. As part of the First ISLSCP (International Satellite Land Surface Climatology Program) Field Experiment (FIFE), we used Regression Tree Analysis to derive an ecological classification of a tall grass prairie landscape. The classification is derived from digital terrain, land use, and land cover data and is based on their association with spectral vegetation indices calculated from single-date and multi-temporal satellite imagery. The regression tree analysis produced a site stratification that is similar to the a priori scheme actually used in FIFE, but is simpler and considerably more effective in reducing sample variance in surface measurements of variables such as biomass, soil moisture and Bowen Ratio. More generally, regression tree analysis is a useful technique for identifying and estimating complex hierarchical relationships in multivariate data sets.     

Land‐use change outweighs projected effects of changing rainfall on tree cover in sub‐Saharan Africa

Global change will likely affect savanna and forest structure and distributions, with implications for diversity within both biomes. Few studies have examined the impacts of both expected precipitation and land use changes on vegetation structure in the future, despite their likely severity. Here, we modeled tree cover in sub‐Saharan Africa, as a proxy for vegetation structure and land cover change, using climatic, edaphic, and anthropic data (R² = 0.97). Projected tree cover for the year 2070, simulated using scenarios that include climate and land use projections, generally decreased, both in forest and savanna, although the directionality of changes varied locally. The main driver of tree cover changes was land use change; the effects of precipitation change were minor by comparison. Interestingly, carbon emissions mitigation via increasing biofuels production resulted in decreases in tree cover, more severe than scenarios with more intense precipitation change, especially within savannas. Evaluation of tree cover change against protected area extent at the WWF Ecoregion scale suggested areas of high biodiversity and ecosystem services concern. Those forests most vulnerable to large decreases in tree cover were also highly protected, potentially buffering the effects of global change. Meanwhile, savannas, especially where they immediately bordered forests (e.g. West and Central Africa), were characterized by a dearth of protected areas, making them highly vulnerable. Savanna must become an explicit policy priority in the face of climate and land use change if conservation and livelihoods are to remain viable into the next century.     

Multi objective land allocation (MOLA) for zoning Ghamishloo Wildlife Sanctuary in Iran

Protected area zoning is an approach towards decreasing conflict between the possible uses of land and providing an opportunity for policy making. GIS data processing and spatial analysis along with decision analysis techniques, were used in this study to define zones for Ghamishloo Wildlife Sanctuary according to I.U.C.N. category IV in Isfahan Province of Iran. We used multi-criteria evaluation and multi-objective land allocation for zoning the sanctuary, which covers an area of about 866 km². First, we prepared a land use map of the area using classification of the IRS 6 (AWiFS) data of May 2005. For zoning this region, nine major criteria including wildlife habitat, vegetation cover, soil, distance to historical places, water resources, road, scenic beauties in the landscape, and also to residential areas, and to the core zone were considered. We used the analytical hierarchy process to derive weights of the criteria and then applied a weighted linear combination technique to combine the factors. The degree of suitability was defined by applying Fuzzy membership function. The wildlife sanctuary was divided into four zones including conservation, recreation, rehabilitation, and cultural zones, consisting of 69%, 21%, 9.5% and 0.5% of the area, respectively. Finally, multi-objective land allocation (MOLA) function was used for allocation of the sanctuary's land area to the zones which produced reasonable results.     

Distribution patterns of boreal marshland birds: modelling the relationships to land cover and climate

Aim The aims of this work were (1) to study how well land‐cover and climatic data are capable of explaining distribution patterns of ten bird species breeding and/or feeding primarily on marshes and other wetlands and (2) to compare the differences between red‐listed and common marshland species in explanatory variables, and to study the predictability of their distribution patterns. Location Finland, northern Europe. Methods The data of the bird atlas survey carried out in 1986–89 using a 10 × 10 km uniform grid system in Finland were used in the analyses. Land‐cover data based on CORINE (Coordination of Information on the Environment) classification and climatic variables were compiled using the same 10 × 10 km grid. Generalized additive models (GAM) with a stepwise selection procedure were used to select relevant explanatory variables and to examine the complexity of the response shapes of the different species to each variable. The original data set was randomly divided into model training (70%) and model evaluation (30%) sets. The final models of common and red‐listed bird species richness were validated by fitting them to the model evaluation set, and the correlation between observed and predicted species richness was calculated. We assessed the discrimination ability of the binary models (single species) with the area under the curve (AUC) of a receiver operating characteristic (ROC) plot and the Kappa coefficient. Results Cover of marshland, shoreline length and mean temperature in April–June were significantly (P < 0.01) related to the common marshland species richness. Cover and clumping of marshland and mean temperature and precipitation in April–June were selected in the model of red‐listed marshland species richness. The level of discrimination in our single species models varied in ROC from fair to excellent (AUC values 0.70–0.95). Cover of marshland was included in all GAM models built for the target species, but clumping of marshland, shoreline length and cover of mires also appeared as important predictors in single species models. Seven species had statistically significant relationships with climatic variables in the multivariate GAMs. Cover of marshland was highest in squares in which the red‐listed bittern Botaurus stellaris, marsh harrier Circus aeruginosus and great reed warbler Acrocephalus arundinaceus and the water rail Rallus aquaticus were observed. Main conclusions Cover of marshland was the only variable which was included in all the models, reinforcing the close connection between the studied species and marshlands. Broad‐scale clumping of marshlands was important for the red‐listed species, probably due to the much lower population sizes of red‐listed species than those of common species. Land‐cover data produced in CORINE seems to be well suited for modelling the distribution patterns of marshland birds. Although climatic variables also strongly affect the studied marshland birds, habitat availability plays a crucial role in their occurrence. The distribution patterns of marshland birds at the scale of 10 × 10 km reflect the interplay between habitat availability and direct climatic variables.     

Water inputs across a tropical montane landscape in Veracruz,Mexico: synergistic effects of land cover,rain and fog seasonality,and interannual precipitation variability

Land‐cover change can alter the spatiotemporal distribution of water inputs to mountain ecosystems, an important control on land‐surface and land‐atmosphere hydrologic fluxes. In eastern Mexico, we examined the influence of three widespread land‐cover types, montane cloud forest, coffee agroforestry, and cleared areas, on total and net water inputs to soil. Stand structural characteristics, as well as rain, fog, stemflow, and throughfall (water that falls through the canopy) water fluxes were measured across 11 sites during wet and dry seasons from 2005 to 2008. Land‐cover type had a significant effect on annual and seasonal net throughfall (NTF <0=canopy water retention plus canopy evaporation; NTF >0=fog water deposition). Forest canopies retained and/or lost to evaporation (i.e. NTF<0) five‐ to 11‐fold more water than coffee agroforests. Moreover, stemflow was fourfold higher under coffee shade than forest trees. Precipitation seasonality and phenological patterns determined the magnitude of these land‐cover differences, as well as their implications for the hydrologic cycle. Significant negative relationships were found between NTF and tree leaf area index (R²=0.38, P<0.002), NTF and stand basal area (R²=0.664, P<0.002), and stemflow and epiphyte loading (R²=0.414, P<0.001). These findings indicate that leaf and epiphyte surface area reductions associated with forest conversion decrease canopy water retention/evaporation, thereby increasing throughfall and stemflow inputs to soil. Interannual precipitation variability also altered patterns of water redistribution across this landscape. Storms and hurricanes resulted in little difference in forest‐coffee wet season NTF, while El Niño Southern Oscillation was associated with a twofold increase in dry season rain and fog throughfall water deposition. In montane headwater regions, changes in water delivery to canopies and soils may affect infiltration, runoff, and evapotranspiration, with implications for provisioning (e.g. water supply) and regulating (e.g. flood mitigation) ecosystem services.